Asset Management, GIS and LiDAR Projects

Menu

TIN

This article was originally written in 2011, but is being re-posted based on recent events…

DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data. They are currently collecting airborne data for approximately 15,000 linear miles of rail. In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.

From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”

The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction. The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company. Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage. All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies. We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data. Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail. The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.

So, back to the discussion about Airborne PTC vs Mobile PTC data. Here is a signal tower collected by Airborne LiDAR. The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.

The next graphic shows the detail of the same Asset feature from the mobile LiDAR data. It is much easier to identify the Asset feature and Type from the point cloud. In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery. By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.

This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.

One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

We have all heard about Asset Management and how it can help an agency extend the useful life of its infrastructure. We all know that in principal it makes all the sense in the world, but the actual application of these concepts require investment in software, hardware and personnel. What we will never know is – How much should we invest in the management of our assets? Using the NERC regulation and the frenzied data collection going on in our industry as an example, consider the following.

Most Airborne LiDAR companies are collecting and delivering data in the $500 – $1,500 per linear mile range, depending on the downstream processing requirements. Most of this data is delivered to the end user as .LAS point clouds, PLS-CADD .BAK, files and some other CAD or GIS formats. Once it is delivered, the agency has a unique opportunity to leverage the delivered products for future value.

If we use Vegetation Encroachment data as an example, we can illustrate how the encroachment information can be used to create a vegetation Asset Class and managed throughout its life-cycle. Most likely, the data delivered to an agency will include .LAS point clouds with classified data reflecting terrain, conductors, towers, buildings, etc. In addition to this, vector data is also delivered and can be used to support maintenance management activities. The graphic below illustrates a common Transmission LiDAR deliverable.

Note the Red vegetation in the graphic above. It shows the vegetation points that have been flagged as encroachment violations based on its proximity to the conductors. These points can then be mapped in a GIS or Asset Management program for further analysis. In doing so, an agency can gather more value from this information. For example, the graphic below illustrates the “grow-in” (light blue) and “fall-in” (red) violations for a section of Transmission line.

GIS mapping provides the user the spatial context necessary to make informed vegetation management decisions. First, the location of vegetation encroachments are known and with a little manipulation, the volume and area of the vegetation can be determined very easily. This gives an agency the ability to control the costs associated with their vegetation management program. Asset management software that leverages GIS can provide the tools necessary to develop an immediate return-on-investment of the software purchase and associated data collection expenditures.

First, the user creates the geospatial layers from the classified point cloud. Vegetation violations can be exported as points and then aggregated into vegetation encroachment units. These units are then integrated with the Work and Asset management system through the use of GIS. Since the geometry of the encroachment units are known based on its GIS attributes, an agency can then determine the following characteristics about their encroachments:

Maximum Height of Encroachment Unit

Average Height of Encroachment Unit

Total Area (acres) of Encroachment Unit

Total Area (acres) of Encroachment Units along a particular circuit

Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed. The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process. In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information. The graphic below shows a KMZ export of Vegetation Encroachments that can be provided to field units in charge of vegetation removal.

A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed. Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is. Pretty amazing concept to embrace because now an agency can accurately predict the costs of their vegetation management program.

Another factor that can be applied to this information is the concept of Risk. Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific Asset Classes and Asset Types. The more critical the Asset – the higher the priority it gets when determining an agency’s primary work focus. In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize its maintenance over less critical assets. By prioritizing using Risk, an agency can take measures to minimize the Risk that exists in its Asset portfolio by fixing these pieces and parts first.

None of this stops once you get to the Work Management piece of the puzzle. I’ll be providing more information related to tracking the work activities as they are completed in the field and using this information to develop more accurate budget forecasts for the future.

DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data. They are currently collecting airborne data for approximately 15,000 linear miles of rail. In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.

From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”

The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction. The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company. Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage. All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies. We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data. Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail. The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.

So, back to the discussion about Airborne PTC vs Mobile PTC data. Here is a signal tower collected by Airborne LiDAR. The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.

The next graphic shows the detail of the same Asset feature from the mobile LiDAR data. It is much easier to identify the Asset feature and Type from the point cloud. In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery. By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.

This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.

One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

DTS/EarthEye just completed a 9-mile mobile LiDAR scan of I-95 here in Florida and provided one of our partners with cross-slope information in a period of days. The data was collected with our buddies at Riegl USA using their VMX-250 mobile LiDAR. This information will be used to generate pavement resurfacing plans for the Florida Department of Transportation (FDOT).

This project shows the value that this type of project can provide to the end user on both sides of the fence.

First, the paving contractor can use this data to develop their 30% plans for submittal to FDOT when bidding on a resurfacing or re-design contract. Having accurate and relevant data related to the roadway’s characteristics gives the paving contractor an edge over the competition because they know what the field conditions are before preparing an over-engineered design specification. This happens all of the time because the detailed field conditions are unknown while they are preparing their plans and they only have historical information to work from.

On the other side of the fence resides the FDOT. They can benefit from this information because if they can provide this detailed information as part of a bid package, they can reap the benefits that are gained from better information. If all contractors have the detailed as-built information (or in this case, accurate cross-slopes), they can all prepare their submittals using the same base information. This will provide the FDOT project manager with more accurate responses based on true field conditions, resulting in more aggressive pricing and decreased project costs.

Here are some screenshots of the information.

LiDAR Data Viewed by Intensity and Corresponding Cross-Slope Profile

Once the data has been collected and calibrated, we generate cross-slopes at a defined interval and export those out as 3D vectors.

These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.

Although this is a pretty simple step, the presentation of the data in Google Earth makes it easy for the end-user to visually identify problem areas and design the corrective actions according to field measurements.

So, I have been working with the guys to keep my feet wet with LiDAR data editing so that I understand more about what it takes to prepare the LiDAR surface for delivery to the client or for an ortho surface. I got my share of data and headed off to edit my surface…

Sounds pretty easy, right? Well, it actually is as long as you know what to look for. Our data for this project had a lot of low points in it – due mostly to the fact that we are shooting down stormwater grates in neighborhoods. This creates low points in the data that is not indicative of the true terrain. We usually filter these out using our filtering algorithms, but sometimes these points still exist in the data and need to be edited out.

The first way to identify a low point is to create a TIN of the surface. If there are low points, the TIN will be dragged down by the surface and there will be a gaping hole in the surface. Another way to identify these holes is to look at the color palette of the scene and if it does not have the usual distribution of colors – Red to Purple – there is a low point somewhere in the scene.

Low Point in TIN Surface

We can also see the low point using the “Profile” view – it can be seen below the surface.

Low Points Below the Filtered Surface

These points can be re-classified and removed from the Ground Classification and placed into the “Low Point / Noise” Classification and then the surface is modified. Note the better distribution of the color palette for the scene…

Resulting TIN Surface

Finally, the resulting profile shows the points reclassified to the correct classification. Repeat for each tile until complete!